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Interwoven, cross-sector, situational and enduring solidarities: crisis, resistance and de-privatisation in care work

2023· article· en· W4366495748 on OpenAlexaffabout
Donna Baines

Bibliographic record

VenueWork in the Global Economy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSolidarityResistance (ecology)Situational ethicsCare workLiminalitySociologyWork (physics)Political sciencePolitical economyPublic relationsGender studiesLaw

Abstract

fetched live from OpenAlex

During and prior to the COVID-19 pandemic, a predominantly female, significantly racialised, activist health and social care union in British Columbia, Canada, built and won decisive resistance/solidarity strategies not only for union members, but also for non-union and previously unionised workers, residents/patients, and the larger careseeking public. The formal and informal strategies were broad, inclusive, values-driven actions that raised wages and conditions while simultaneously extending and improving care. The article draws on and extends concepts used in moral economy and labour process theorising to argue that the three interlaced, gendered resistance strategies reflected interwoven, cross-sector, situational and enduring solidarities. The analysis in this article highlights the almost indivisible aspects of formal and informal, long contention and situational resistance strategies, and suggests that a moral economy of ‘care’ and ‘restoring fairness’ formed the core of the entwined solidarity narratives and resistance strategies successfully coming together during the time of COVID-19. The article contributes to further theorising of gendered resistance (informal, liminal, unpaid work), the moral economy and care work as part of the labour process (interwoven solidarity), and adds to theorising long resistance strategies (enduring solidarity), and situational resistance strategies (cross-sector solidarity) in care work contexts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.378
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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